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  1. 1
    Direct approach for mining association rules from structured XML data

    Direct approach for mining association rules from structured XML data by Abazeed, Ashraf Riad

    Published 2012
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  2. 2
    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection

    Separator Database and SPM Tree Framework for Mining Sequential Patterns Using Prefixspan with Pseudoprojection by Saputra , Dhany, Rambli, Dayang R.A., Foong, Oi Mean

    Published 2008
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  3. 3
    Study and Implementation of Data Mining in Urban Gardening

    Study and Implementation of Data Mining in Urban Gardening by Mohana, Muniandy, Lee, Eu Vern

    Published 2019
    “…Attached sensors generate data and send these data to the Java Servlet application through a WIFI module. These data are processed and stored in appropriate formats in a MySQL server database. …”
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  4. 4
    Mining Sequential Patterns Using I-PrefixSpan

    Mining Sequential Patterns Using I-PrefixSpan by Dhany , Saputra, Rambli Dayang, R.A., Foong, Oi Mean

    Published 2007
    “…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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  5. 5
    A web-based implementation of k-means algorithms

    A web-based implementation of k-means algorithms by Lee, Quan

    Published 2022
    “…This stinginess of proximity measures in data mining tools is stifling the performance of the algorithm. …”
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    Final Year Project / Dissertation / Thesis
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  6. 6
    Prediction of customer churn for ABC Multistate Bank using machine learning algorithms / Hui Shan Hon ... [et al.]

    Prediction of customer churn for ABC Multistate Bank using machine learning algorithms / Hui Shan Hon ... [et al.] by Hui, Shan Hon, Khai, Wah Khaw, XinYing, Chew, Wai, Peng Wong

    Published 2023
    “…Customer churn is defined as the tendency of customers to cease doing business with a company in a given period. ABC Multistate Bank faces the challenges to hold clients. The purpose of this study is to apply machine learning algorithms to develop the most effective model for predicting bank customer churn. …”
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  7. 7
    Mining Sequential Patterns using I-PrefixSpan

    Mining Sequential Patterns using I-PrefixSpan by Saputra , Dhany, Dayang R.A. Rambli, Foong, Oi Mean

    Published 2008
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    Citation Index Journal
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  8. 8
    The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework

    The Determinant Factors for the Issuance of Central Bank Digital Currency (CBDC) in Malaysia using Machine Learning Framework by Normi Sham Awang, Abu Bakar, Norzariyah, Yahya, Norbik Bashah, Idris, Engku Rabiah Adawiah, Engku Ali, Jasni, Mohamad Zain, Erni Eliana, Khairuddin, Ahmad Firdaus, Zainal Abidin, Murtaj, Sheikh Mohammad Tahsin, Siti Sarah, Maidin

    Published 2024
    “…The overall CentralBank Digital Currency Project Index (CBDCPI) was selected as a target variable,while two machine learning algorithms, Random Forest and XGBoost were utilized to identify the determining variables. …”
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  9. 9
    The determinant factors for the issuance of Central Bank Digital Currency (CBDC) in Malaysia using machine learning framework

    The determinant factors for the issuance of Central Bank Digital Currency (CBDC) in Malaysia using machine learning framework by Awang Abu Bakar, Normi Sham, Yahya, Norzariyah, Idris, Norbik Bashah, Engku Ali, Engku Rabiah Adawiah, Mohamad Zain, Jasni, Khairuddin, Erni Eliana, Zainal Abidin, Ahmad Firdaus, Murtaj, Sheikh Mohammad Tahsin, Maidin, Siti Sarah

    Published 2024
    “…The overall Central Bank Digital Currency Project Index (CBDCPI) was selected as a target variable, while two machine learning algorithms, Random Forest and XGBoost were utilized to identify the determining variables. …”
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  10. 10
    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning

    Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning by Lim, Wai Ping, Goh, Ching Pang

    Published 2023
    “…This research explores the application of unsupervised learning, a subset of Artificial Intelligence (AI), to analyze customer behavior in accepting personal loans within the banking sector. …”
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  11. 11
    Features selection for intrusion detection system using hybridize PSO-SVM

    Features selection for intrusion detection system using hybridize PSO-SVM by Tabaan, Alaa Abdulrahman

    Published 2016
    “…The simulation will be carried on WEKA tool, which allows us to call some data mining methods under JAVA environment. The proposed model will be tested and evaluated on both NSL-KDD and KDD-CUP 99 using several performance metrics.…”
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  12. 12
    Evaluating Machine Learning Algorithms for Fake Currency Detection

    Evaluating Machine Learning Algorithms for Fake Currency Detection by Keerthana, S.N, Chitra, K.

    Published 2024
    “…In this study, we evaluate the effectiveness of six supervised machine learning algorithms—K-Nearest Neighbor, Decision Trees, Support Vector Machine, Random Forests, Logistic Regression, and Naive Bayes—in detecting the authenticity of banknotes. …”
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  13. 13
    Development of unconstrained handwritten digit extraction, segmentation and recognition on bank cheques using artificial neural network

    Development of unconstrained handwritten digit extraction, segmentation and recognition on bank cheques using artificial neural network by Francis, Adam

    Published 2005
    “…The third objective is to perform Vertical Splitting Algorithm technique for digit segmentation. And lastly, to develop an Artificial Neural Network for digit recognition. …”
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  14. 14
    Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023

    Loan default prediction using machine learning algorithms: a systematic literature review 2020 -2023 by Soomro, Anam, Zakariyah, Habeebullah, Aftab, S.M.A., Muflehi, Mohamad, Shah, Asadullah, Meraj, Syeda

    Published 2024
    “…This study conducts a systematic literature review (SLR) on the prediction of loan defaults using machine learning algorithms (MLAs) from 2020 to 2023. It critically examines the transition from traditional statistical models to advanced ML techniques in assessing credit risk, with a focus on the banking sector's need for reliable default prediction methods. …”
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  15. 15
    Loss minimization in load flow simulation in power system

    Loss minimization in load flow simulation in power system by Mohamad Firdaus, Mat Hussin

    Published 2009
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    Learning Object
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  16. 16
    Machine learning based return prediction for digital financial portfolios

    Machine learning based return prediction for digital financial portfolios by LinXi Shi, Thien Sang Lim, Jin Yan, Pengcheng Qi, Tao Li

    Published 2025
    “…The machine learning algorithm is introduced to optimize the digital financial portfolio investment return prediction system. …”
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  17. 17
    Hybrid BLEU Algorithm For Structured Exam Management System

    Hybrid BLEU Algorithm For Structured Exam Management System by Zulhana, Zulkifle

    Published 2008
    “…Due to this problem, "Hybrid BLEU algorithm for Structured Exam Management System " is develop to aid the lecturers during assessment in the construction of quiz and test. …”
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  18. 18
    Deep learning model for predicting and detecting overlapping symptoms of cardiovascular diseases in hospitals of UAE

    Deep learning model for predicting and detecting overlapping symptoms of cardiovascular diseases in hospitals of UAE by Abbas Alhadeethy, Najwa Fadhil, Khedher, Akram M Z M, Shah, Asadullah

    Published 2012
    “…The use of this learning technique has been increased in various domains such as e-commerce, banking and finance, as well as for speech and feature recognition to learn and classify intricate information. …”
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  19. 19
    Suspicious activities detection for anti-money laundering using machine learning techniques

    Suspicious activities detection for anti-money laundering using machine learning techniques by Lim, Aun Chir

    Published 2025
    “…To solve money laundering, more effective techniques for detecting suspicious transactions must be developed. Machine learning is able to learn complex relationships within large datasets then identify anomalies that deviate from well-defined patterns. …”
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  20. 20
    Review of the machine learning methods in the classification of phishing attack

    Review of the machine learning methods in the classification of phishing attack by Jupin, John Arthur, Sutikno, Tole, Mohd Arfian, Ismail, Mohd Saberi, Mohamad, Shahreen, Kasim, Deris, Stiawan

    Published 2019
    “…There are several algorithms that can be used in the machine learning method to prevent these attacks. …”
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